scholarly journals Cis-Regulatory Hubs: a Relevant 3D Model to Study the Genetics of Complex Diseases with an Application to Schizophrenia

2021 ◽  
Author(s):  
Loic Mangnier ◽  
Charles Joly-Beauparlant ◽  
Arnaud Droit ◽  
Steve Bilodeau ◽  
Alexandre Bureau
Keyword(s):  
Complex Traits ◽  
3D Model ◽  
Complex Disease ◽  
Complex Diseases ◽  
3D Models ◽  
Gene Promoters ◽  
Disease Etiology ◽  
3 Dimensional ◽  
Regulatory Processes ◽  
Chromatin Interactions

Background: The 3-dimensional (3D) conformation of the chromatin creates complex networks of noncoding regulatory regions (distal elements) and genes with important implications in gene regulation. Despite the importance of the role of noncoding regions in complex traits, little is known about their interplay within regulatory hubs and the implication in multigenic diseases like schizophrenia. Results: Here we show that cis-regulatory hubs (CRHs) in neurons highlight functional interactions between distal elements and promoters, providing a model to explain the epigenetic mechanisms involved in complex diseases. CRHs represent a new 3D model, where several distal elements interact to create a complex network of active genes. Indeed, we found that CRHs represent functional structures, showing higher transcriptional activity. In a disease context, CRHs highlighted strong enrichments in schizophrenia-associated genes, schizophrenia-associated SNPs and schizophrenia heritability compared to equivalent tissue and non-tissue-specific structures. Also, genes, by sharing the same distal elements, converge to common biological processes associated with schizophrenia. Finally, the results showed that in a complex disease etiology, small CRHs by linking fewer distal elements to promoters constitute a more informative structure than larger hubs. Conclusion: CRHs are a new 3D model of the chromatin interactions between gene promoters and their distal elements highlighting causal regulatory processes and providing a better understanding of complex disease etiology such as schizophrenia. Indeed, by providing a finer scale chromosome architecture, we have genetic and statistical evidence that CRHs represent a major advancement in 3D models to study the epigenetic underlying processes involved in complex traits.

scholarly journals Evaluating the informativeness of deep learning annotations for human complex diseases

2019 ◽  
Author(s):  
Kushal K. Dey ◽  
Bryce Van de Geijn ◽  
Samuel Sungil Kim ◽  
Farhad Hormozdiari ◽  
David R. Kelley ◽  
...  
Keyword(s):  
Deep Learning ◽  
Complex Traits ◽  
Complex Disease ◽  
Complex Diseases ◽  
Great Promise ◽  
Full Potential ◽  
Learning Models ◽  
Allelic Effect ◽  
Meta Analyses ◽  
Human Complex

AbstractDeep learning models have shown great promise in predicting genome-wide regulatory effects from DNA sequence, but their informativeness for human complex diseases and traits is not fully understood. Here, we evaluate the disease informativeness of allelic-effect annotations (absolute value of the predicted difference between reference and variant alleles) constructed using two previously trained deep learning models, DeepSEA and Basenji. We apply stratified LD score regression (S-LDSC) to 41 independent diseases and complex traits (average N=320K) to evaluate each annotation’s informativeness for disease heritability conditional on a broad set of coding, conserved, regulatory and LD-related annotations from the baseline-LD model and other sources; as a secondary metric, we also evaluate the accuracy of models that incorporate deep learning annotations in predicting disease-associated or fine-mapped SNPs. We aggregated annotations across all tissues (resp. blood cell types or brain tissues) in meta-analyses across all 41 traits (resp. 11 blood-related traits or 8 brain-related traits). These allelic-effect annotations were highly enriched for disease heritability, but produced only limited conditionally significant results – only Basenji-H3K4me3 in meta-analyses across all 41 traits and brain-specific Basenji-H3K4me3 in meta-analyses across 8 brain-related traits. We conclude that deep learning models are yet to achieve their full potential to provide considerable amount of unique information for complex disease, and that the informativeness of deep learning models for disease beyond established functional annotations cannot be inferred from metrics based on their accuracy in predicting regulatory annotations.

scholarly journals Genetics of Complex Disease: From Mouse to Man and Back

1995 ◽  
Vol 9 (3) ◽  
pp. 169-174
Author(s):  
Craig H Warden ◽  
Jerome I Rotter
Keyword(s):  
Complex Traits ◽  
Complex Disease ◽  
Genome Mapping ◽  
Complex Diseases ◽  
Rapid Identification ◽  
Whole Genome ◽  
Donor Strain ◽  
Congenic Strains ◽  
Background Strain ◽  
Combined Application

Identification of genes underlying complex traits has been difficult, but combined application of novel methods and mouse models provides new hope. Rare monogenic syndromes, and candidate gene and biochemical approaches are sometimes useful, but each of these approaches also has limitations. Some problems that prevent identification and isolation of genes underlying complex disease can be avoided by the use of whole genome mapping of mouse crosses or of human families. Mice have many advantages for the study of complex disease, including an extensive genetic map. A generic method has recently been developed and applied for detection of quantitative trait loci (QTLs) using whole genome maps of mouse crosses. Availability of more than 200 congenic strains provides another incentive for studies in mice. Congenic strains provide a rich, but previously unexploited, resource for the rapid identification of genes causing complex diseases. A congenic mouse strain is genetically identical to a background strain, except for a small chromosomal region derived from a donor strain. Thus, comparison of a phenotype in a congenic strain with the phenotype in its background strain allows study of the effects of single genes derived from the donor strain, isolated from the effects of other donor strain genes. Application of all or several techniques to complex disease studies in mice and in humans may lead to the identification and understanding of complex diseases whose etiology is currently unknown.

scholarly journals Impact of 3-dimensional genome organization, guided by cohesin and CTCF looping, on sex-biased chromatin interactions and gene expression in mouse liver

2020 ◽  
Author(s):  
Bryan J Matthews ◽  
David J Waxman
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Binding Sites ◽  
Genome Organization ◽  
Mouse Liver ◽  
Ctcf Binding ◽  
Gene Promoters ◽  
3 Dimensional ◽  
Chromatin Interactions ◽  
The Impact

Abstract Background: Sex differences in the transcriptome and epigenome are widespread in mouse liver and are associated with sex-bias in liver disease. Several thousand sex-differential distal enhancers have been identified; however, their links to sex-biased genes and the impact of any sex-differences in nuclear organization, DNA looping, and chromatin interactions are unknown.Results: To address these issues, we first characterized 1,847 mouse liver genomic regions showing significant sex differential occupancy by cohesin and CTCF, two key 3D nuclear organizing factors. These sex-differential binding sites were largely distal to sex-biased genes, but rarely generated sex-differential TAD (topologically associating domain) or intra-TAD loop anchors. A substantial subset of the sex-biased cohesin-non-CTCF binding sites, but not the sex-biased cohesin-and-CTCF binding sites, overlapped sex-biased enhancers. Cohesin depletion reduced the expression of male-biased genes with distal, but not proximal, sex-biased enhancers by >10-fold, implicating cohesin in long-range enhancer interactions regulating sex-biased genes. Using circularized chromosome conformation capture-based sequencing (4C-seq), we showed that sex differences in distal sex-biased enhancer-promoter interactions are common. Sex-differential chromatin interactions involving sex-biased gene promoters, enhancers, and lncRNAs were associated with sex-biased binding of cohesin and/or CTCF. Furthermore, intra-TAD loops with sex-independent cohesin-and-CTCF anchors conferred sex specificity to chromatin interactions indirectly, by insulating sex-biased enhancer-promoter contacts and by bringing sex-biased genes into closer proximity to sex-biased enhancers.Conclusions: These findings elucidate how 3-dimensional genome organization contributes to sex differences in gene expression in a non-reproductive tissue through both direct and indirect effects of cohesin and CTCF looping on distal enhancer interactions with sex-differentially expressed genes.

scholarly journals Computational Methods for the Discovery of Metabolic Markers of Complex Traits

Metabolites ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 66 ◽  
Author(s):  
Michael Lee ◽  
Ting Hu
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Complex Traits ◽  
Complex Disease ◽  
Physiological State ◽  
Complex Diseases ◽  
Diagnostic Procedures ◽  
Machine Learning Algorithms ◽  
Metabolic Markers ◽  
Metabolic Marker

Metabolomics uses quantitative analyses of metabolites from tissues or bodily fluids to acquire a functional readout of the physiological state. Complex diseases arise from the influence of multiple factors, such as genetics, environment and lifestyle. Since genes, RNAs and proteins converge onto the terminal downstream metabolome, metabolomics datasets offer a rich source of information in a complex and convoluted presentation. Thus, powerful computational methods capable of deciphering the effects of many upstream influences have become increasingly necessary. In this review, the workflow of metabolic marker discovery is outlined from metabolite extraction to model interpretation and validation. Additionally, current metabolomics research in various complex disease areas is examined to identify gaps and trends in the use of several statistical and computational algorithms. Then, we highlight and discuss three advanced machine-learning algorithms, specifically ensemble learning, artificial neural networks, and genetic programming, that are currently less visible, but are budding with high potential for utility in metabolomics research. With an upward trend in the use of highly-accurate, multivariate models in the metabolomics literature, diagnostic biomarker panels of complex diseases are more recently achieving accuracies approaching or exceeding traditional diagnostic procedures. This review aims to provide an overview of computational methods in metabolomics and promote the use of up-to-date machine-learning and computational methods by metabolomics researchers.

Discussion on Modeling Approach of 3-Dimensional Models Based on Web3D

2013 ◽  
Vol 300-301 ◽  
pp. 261-264
Author(s):  
Xiao Ping Yang ◽  
Wei Ping Hu ◽  
Jing Jing Wang
Keyword(s):  
3D Model ◽  
Technology Development ◽  
Texture Mapping ◽  
3D Models ◽  
Interactive Design ◽  
The Internet ◽  
3 Dimensional ◽  
Modeling Methods ◽  
Internet Browser ◽  
On Line

Web3D can be understood as 3D model displaying for the Internet browser. In the regard of Web3D technology, to name a few, modeling of 3D models, texture mapping of 3D models, interactive design will pose influence on the final on-line displaying. The paper discusses feasibility of Web3D technology development from modeling methods for Web3D models.

scholarly journals Impact of 3-dimensional genome organization, guided by cohesin and CTCF looping, on sex-biased chromatin interactions and gene expression in mouse liver

2019 ◽  
Author(s):  
Bryan J. Matthews ◽  
David J. Waxman
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Binding Sites ◽  
Genome Organization ◽  
Mouse Liver ◽  
Ctcf Binding ◽  
Gene Promoters ◽  
3 Dimensional ◽  
Chromatin Interactions ◽  
The Impact

AbstractBackgroundSex differences in the transcriptome and epigenome are widespread in mouse liver and are associated with sex-bias in liver disease. Several thousand sex-differential distal enhancers have been identified; however, their links to sex-biased genes and the impact of any sex-differences in nuclear organization, DNA looping, and chromatin interactions are unknown.ResultsTo address these issues, we first characterized 1,847 mouse liver genomic regions showing significant sex differential occupancy by cohesin and CTCF, two key 3D nuclear organizing factors. These sex-differential binding sites were largely distal to sex-biased genes, but rarely generated sex-differential TAD (topologically associating domain) or intra-TAD loop anchors. A substantial subset of the sex-biased cohesin-non-CTCF binding sites, but not the sex-biased cohesin-and-CTCF binding sites, overlapped sex-biased enhancers. Cohesin depletion reduced the expression of male-biased genes with distal, but not proximal, sex-biased enhancers by >10-fold, implicating cohesin in long-range enhancer interactions regulating sex-biased genes. Using circularized chromosome conformation capture-based sequencing (4C-seq), we showed that sex differences in distal sex-biased enhancer-promoter interactions are common. Sex-differential chromatin interactions involving sex-biased gene promoters, enhancers, and lncRNAs were associated with sex-biased binding of cohesin and/or CTCF. Furthermore, intra-TAD loops with sex-independent cohesin-and-CTCF anchors conferred sex specificity to chromatin interactions indirectly, by insulating sex-biased enhancer-promoter contacts and by bringing sex-biased genes into closer proximity to sex-biased enhancers.ConclusionsThese findings elucidate how 3-dimensional genome organization contributes to sex differences in gene expression in a non-reproductive tissue through both direct and indirect effects of cohesin and CTCF looping on distal enhancer interactions with sex-differentially expressed genes.

In vitro validation of a 3-dimensional transrectal ultrasound system for prostate biopsiess

2007 ◽  
Vol 30 (4) ◽  
pp. 77
Author(s):  
Derek Cool ◽  
Shi Sherebrin ◽  
Jonathan Izawa ◽  
Joseph Chin ◽  
Aaron Fenster
Keyword(s):  
Prostate Biopsy ◽  
3D Model ◽  
Transrectal Ultrasound ◽  
Surface Topology ◽  
Sufficient Information ◽  
Needle Guidance ◽  
3 Dimensional ◽  
High Incidence ◽  
3D Information

Introduction: Transrectal ultrasound (TRUS) prostate biopsy (Bx) is currently confined to 2D information to both target and record 3D Bx locations. Accurate placement of Bx needles cannot be verified without 3D information, and recording Bx sites in 2D does not provide sufficient information to accurately guide the high incidence of repeat Bx. We have designed a 3D TRUS prostate Bx system that augments the current 2D TRUS system and provides tools for biopsy-planning, needle guidance, and recording of the biopsy core locations entirely in 3D. Methods: Our Bx system displays a 3D model of the patient’s prostate, which is generated intra-procedure from a collection of 2D TRUS images, representative of the particular prostate shape. Bx targets are selected, needle guidance is facilitated, and 3D Bx sites are recorded within the 3D context of the prostate model. The complete 3D Bx system was validated, in vitro, by performing standard ten-core Bx on anatomical phantoms of two patient’s prostates. The accuracy of the needle-guidance, Bx location recording, and 3D model volume and surface topology were validated against a CT gold standard. Results: The Bx system successfully reconstructed the 3D patient prostate models with a mean volume error of 3.2 ± 7.6%. Using the 3D system, needles were accurately guided to the pre-determined targets with a mean error of 2.26 ± 1.03 mm and the 3D locations of the Bx cores were accurately recorded with a mean distance error of 1.47 ± 0.79 mm. Conclusion: We have successfully developed a 3D TRUS prostate biopsy system and validated the system in vitro. A pilot study has been initiated to apply the system clinically.

Endograft Planning without Preoperative Arteriography: A Clinical Feasibility Study

2000 ◽  
Vol 7 (1) ◽  
pp. 8-15 ◽  
Author(s):  
Hugh G. Beebe ◽  
Boonprasit Kritpracha ◽  
Sharon Serres ◽  
John P. Pigott ◽  
Charles I. Price ◽  
...  
Keyword(s):  
3D Model ◽  
Preoperative Evaluation ◽  
Spiral Ct ◽  
Side Branch ◽  
Aortic Aneurysms ◽  
Slice Thickness ◽  
Technical Success Rate ◽  
3 Dimensional ◽  
Procedural Time ◽  
Procedural Success

Purpose: To investigate an alternative method of preprocedural planning for aortic endografting based solely on spiral computed tomography (CT) with 3-dimensional (3D) reconstruction without preoperative arteriography. Methods: From August 1997 to April 1998, 25 consecutive patients with abdominal aortic aneurysms (AAA) were evaluated for endovascular repair by spiral CT scans (2-mm slice thickness) and computerized 3D model construction. No additional imaging for planning was performed. The aortoiliac dimensions, thrombus load, calcification, and vessel tortuosity were measured and evaluated from the 3D model of the aortoiliac segment. These data were used for selecting the patients; the configuration, diameter, and length of the endograft; and the attachment sites for deployment. Results: Primary procedural success was 92% (23/25). All endografts were deployed as planned, and there were no conversions to open repair. Six patients required adjunctive procedures for delivery system access or for iliac aneurysm exclusion, as predicted by the 3D model. Mean procedural time was 91 minutes (range 24 to 273). Two (8%) type II (side branch) endoleaks both sealed spontaneously within 1 month. No graft-related complications or death occurred, for a 30-day technical success rate of 100%. Conclusions: This computerized 3D model provided accurate data for preoperative evaluation of the aortoiliac segment for endovascular AAA repair. Satisfactory technical outcomes for aortic endografts can be achieved without the use of preprocedural invasive imaging.

scholarly journals 3D reconstruction of genomic regions from sparse interaction data

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Julen Mendieta-Esteban ◽  
Marco Di Stefano ◽  
David Castillo ◽  
Irene Farabella ◽  
Marc A Marti-Renom
Keyword(s):  
3D Models ◽  
Genomic Region ◽  
Gene Promoters ◽  
Chromosome Conformation ◽  
The Matrix ◽  
Chromatin Structural ◽  
A Cell ◽  
Similar Accuracy ◽  
Genomic Regions ◽  
Interaction Matrices

Abstract Chromosome conformation capture (3C) technologies measure the interaction frequency between pairs of chromatin regions within the nucleus in a cell or a population of cells. Some of these 3C technologies retrieve interactions involving non-contiguous sets of loci, resulting in sparse interaction matrices. One of such 3C technologies is Promoter Capture Hi-C (pcHi-C) that is tailored to probe only interactions involving gene promoters. As such, pcHi-C provides sparse interaction matrices that are suitable to characterize short- and long-range enhancer–promoter interactions. Here, we introduce a new method to reconstruct the chromatin structural (3D) organization from sparse 3C-based datasets such as pcHi-C. Our method allows for data normalization, detection of significant interactions and reconstruction of the full 3D organization of the genomic region despite of the data sparseness. Specifically, it builds, with as low as the 2–3% of the data from the matrix, reliable 3D models of similar accuracy of those based on dense interaction matrices. Furthermore, the method is sensitive enough to detect cell-type-specific 3D organizational features such as the formation of different networks of active gene communities.

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